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Lévy Based Cox Point Processes

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Lévy Based Cox Point Processes Lévy based Cox point processes Gunnar Hellmund PhD Thesis Department of Mathematical Sciences University of Aarhus February 2009 To Katharina and Johanna. Acknowledgments I would like to thank my family for their love and support during my three years as a PhD student. Especially I want to thank my wife Lene: I am humbled by your love. I wish to thank my supervisor Eva B. Vedel Jensen for originally encour- aging me to initiate the PhD study and introducing me to the theory of point processes. I also wish to thank Rasmus Plenge Waagepetersen and Svend-Erik Graversen for their kind advise and commitment to science. 3 Preface This thesis consists of three papers. The rst paper Lévy based Cox point processes includes background material, basic denitions and other core ma- terial. The second paper Completely random signed measures answers some fundamental probability theoretical questions regarding Lévy bases, while the third paper Strong mixing with a view toward spatio-temporal estimating functions is concerned with estimation theory for (non-stationary) Cox point processes. Before the actual papers, I provide an informal readers guide as introduc- tion where I put emphasis on the ndings I favor. 4 Contents • A readers guide • Paper 1: Lévy based Cox point processes (30 pages) • Paper 2: Completely random signed measures (8 pages) • Paper 3: Strong mixing with a view toward spatio-temporal estimating functions (15 pages) Lévy based Cox point processes has been published in Advances in Applied Probability vol. 40 nr. 3, s. 603-629 Completely random signed measures will be published in Statistics and Prob- ability Letters (in press) Strong mixing with a view toward spatio-temporal estimating functions will appear as a Thiele Research Report and will be submitted to Statistica Sinica for the upcoming special issue on composite likelihood methods. 5 A readers guide Below, I give an informal readers guide to the three papers of the thesis. Section number etc. refer to the paper in question. References can be found immediately after the readers guide. Paper 1: Lévy based Cox point processes Cox point processes are point processes with random intensity, i.e. given a realization of the generating random eld Λ = λ the resulting point process is a Poisson point process with intensity λ. The most widely used Cox point process models are log-Gaussian Cox processes (LGCPs) driven by log-Gaussian random elds and shot-noise Cox processes (SNCPs) driven by shot-noise random elds. The introduction of Paper 1 provides a short discussion of the literature and points out the major result that both LGCPs and SNCPs can be constructed through kernel smoothings of so-called Lévy bases. A Lévy basis L dened on Rd is a stochastic process indexed by the bounded Borel subsets of Rd such that • Its values on disjoint sets are independent • The values of L are innitely divisible • Given any disjoint sequence of bounded Borel sets (An) such that [An is a bounded Borel subset X L ([An) = L (An) ;P − a:s: Section 2 provides theoretical background material which also enables the reader to understand integration wrt. Lévy bases. Since the random elds driving (log) Lévy based Cox point processes are kernel smoothings of Lévy bases this is an important Section. Lemma 1 gives sucient conditions for integrability of a kernel wrt. a Lévy basis. Necessary and sucient conditions for integrability are given in [2], but their conditions are not as user-friendly as our conditions. For readers unfamiliar with Cox point processes some key concepts are summarized in Section 3. The denition of Lévy driven Cox processes is given in Section 4.1. Sec- tion 4.2 provides nth order product densities (Proposition 3), and probability generating functional (Proposition 6). A mixing result (Proposition 7) that 6 might be interesting for inference of stationary point processes is given in Sec- tion 4.3, while Section 4.4 provides examples of Lévy driven Cox processes incl. the important SNCPs. The denition of log Lévy driven Cox Processes is given in Section 5.1. As shown in Section 5.4.2. these include log-Gaussian Cox point processes driven by stationary Gaussian random elds - but it also includes the log shot-noise Cox processes and combinations of these two classes as discussed in Section 5.1. A traditional argument for using LGCPs is that the Gaussian random eld model random events - events that make the intensity increase or decrease. Using a stationary Gaussian random eld will however on average provide equal increases and decreases of the intensity. Log shot-noise random elds can be used to separate (and quantify) the amount of respectively increases and decreases in the intensity generated by random events. The use of a positive shot-noise random eld in a log Lévy driven Cox process can describe the positive eect on the intensity by random events and a negative shot-noise random eld can be used to describe the negative eect on the intensity by random events. (see gure 3 on page 21 of Paper 1). Section 5.2 provides details on nth order product densities while a mixing result (useful in a stationary setting) is provided by Proposition 11 in Section 5.3. Finally in Section 6 combinations of Lévy driven Cox processes and log Lévy driven Cox processes are given. Section 7 discusses inhomogeneous (log) Lévy driven Cox processes. In the concluding discussion in Section 8 spatio-temporal extensions are given in Section 8.2 and in Section 8.3 inference based on summary statistics is mentioned. 7 Paper 2: Completely random signed measures A Lévy basis can be seen as a 'generalized' random measure. It is however not a random (signed) measure in the usual sense, since its realizations may not be (signed) Radon measures. A question to be answered is: How fundamental is the concept of a Lévy basis and how is it related to the usual notion of random measures? These questions are answered in the paper Completely random signed measures. According to Lemma 3.1 the assumption of innitely divisible values of Lévy bases (that might seem odd at rst) is a natural consequence of the independence assumption, if the values of the Lévy basis on bounded Borel sets are assumed to have nite variance or the Lévy basis is dominated by Lebesgue measure. The proof of Lemma 3.1 uses a result in [1] and a trans- lation of the problem to a one dimensional stochastic process setting, a trick that is also used in other proofs. The question: 'When is a Lévy basis a (signed) random measure?' is answered by Corollary 5.5 (in conjunction with Lemma 4.6 and Denition 5.4). As a side-eect of these investigations we were able to give a characteri- zation of completely random signed measures that extends known results for completely random measures (Theorem 4.7). 8 Paper 3: Strong mixing with a view toward spatio-temporal estimating functions As described in Lévy based Cox point processes the Lévy based Cox processes constitute a large and exible class of point processes driven by random elds. Most often second-order reweighted stationarity or even other kinds of non- stationary models are natural to consider in an applied situation. As noted in Section 1.1 the recent literature on Cox point processes focuses on second- order reweighted stationary processes on the form Λ(x) = exp (β · z (x)) Ψ (x) ; (1) where β is a parameter vector, z is covariate information and Ψ is a stationary eld. The literature on Cox point processes dened on (rectangular) observa- tion windows in R2 has so far used minimum contrast methods for summary statistics to obtain parameter estimates - most recently in [3]. A major draw- back of these methods is that the summary statistics are only well-dened if the Cox point process under investigation is stationary or second-order re-weighted stationary. Instead of using summary statistics, the Bernoulli composite likelihood introduced in [4] can be used in much more general settings (see Section 2.1). However departing from stationarity introduces diculties when proving asymptotic results. The concept of strong mixing that is essential for consis- tency and asymptotic normality (Section 2.2) of parameter estimates in the non-stationary setting is introduced in Section 1.1. For the rst time in the literature on Cox point processes dened on (rectangular) observation windows in R2 we obtain parameter estimates for a process which is neither stationary nor second-order re-weighted station- ary (see the short simulation study in Section 2.3). The fact that parameter estimates can be obtained in a non-stationary setting will make Cox point processes more attractive when modeling the dynamics of natural phenom- ena. Furthermore we introduce a exible class of shot-noise Cox processes based on the (in geostatistical context) popular Whittle-Matern family of covariance functions (Section 1.2). The exible second-order structure of this family of stationary random shot-noise elds makes it a good choice of the eld Ψ in formula (1), when Cox point processes are used to obtain information on the eect of covariates. 9 References [1] S. J. Karlin and W. J. Studdun. Tchebyche systems: With Applications in Analysis and Statistics. Pure and applied mathematics. New York: Wiley, 1966. [2] R.S. Rajput and J. Rosinski. Spectral representation of innitely divisible processes. Probab. Theory Relat. Fields, 82:451487, 1989. [3] R. Waagepetersen and Y. Guan. Two-step estimation for inhomogeneous spatial point processes. submitted, 2008. [4] R. P. Waagepetersen. An estimating function approach to inference for in- homogeneous neyman-scott processes. Biometrics, 63(1):252258, March 2007.
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